Regtech startup’s mission: Find real terrorists, avoid false alarms

It was an understandable, knee-jerk reaction after a scolding by regulators.

When examiners from the Office of the Comptroller of the Currency turned up anti-money-laundering deficiencies at Citigroup during a compliance audit, the bank bought an anti-laundering system with all the bells and whistles from an enterprise software vendor.

While the new system was necessary, it wasn’t exactly effective, says Hans Morris, then Citi’s chief financial officer for markets and banking.

“I felt the return on our effort and investment was terrible,” recalled Morris, who is now managing director of the venture capital firm Nyca Partners. “It was a massive investment, but the main result was a large increase in false positives. I remember asking how many terrorists we were detecting, and the answer was that there was not much of a difference.”

Such false alarms — and more importantly the money banks are spending to fix them — have attracted the attention of fintech executives who believe artificial intelligence can help turn thousands of possibly misleading red flags into a handful of meaningful alerts.

On Wednesday morning, Konrad Alt, a former regulator and chief operating officer of Promontory Financial Group, and artificial intelligence expert Bradford Cross are launching a startup called Merlon Intelligence that they say can do this.

For those unfamiliar with the term, a merlon is the wall part of the zig-zag structure at the top of medieval castles. Think where a guard would shield himself between firing arrows. In other words, merlons represent safety, says Alt, who came up with the name.

Nyca is among the startup’s backers.

“This area is clearly ripe for an entire redesign of existing processes,” Morris said. “Even though banks believe they are very diligent in following KYC/AML requirements, it’s very inefficient, despite very large investment. And it’s not just fear of being fined; no bank wants to enable illicit use of the financial system either.”

Merlon’s technology is focused on finding the needle rather than adding more hay to the haystack, Cross said.

It plans to do that by improving upon identity verification by mapping connections between people inside and outside of the United States and monitoring their relationships. It monitors the movement of money for anything that might look like money laundering or other kinds of foul play. It conducts the negative news screening banks are required to do— literally scanning news stories for mentions of customers or potential customers associated with wrongdoing.

Merlon uses AI to rank and filter AML alerts coming from what may be a “naive, rules-based system,” Cross said. “There’s nothing that says you can’t comply with all those rules and still be intelligent about it, where you can demonstrably prove that you’re not creating false negatives.”

The machine-learning engine pushes the highest-risk news stories and alerts to the top. It highlights the most important information, so the analyst’s attention is drawn immediately to the most relevant parts. (For example, a news story about a potential customer might have the person’s name highlighted in one color and the risky topic they’re affiliated with, like tax evasion or fraud, in another.)

Jon Lehr, co-founder and general partner at Work-bench, one of Merlon’s venture capital backers alongside Nyca, heaps praise on the company’s founders.

“You’re taking Bradford Cross, one of the best machine-learning gurus from the West Coast, and pairing him with Konrad Alt, the former COO of Promontory, the big regulatory services firm that was snapped up by IBM,” Lehr said. “What’s so cool is that you’re combining best-of-breed machine learning with top domain expertise, and that’s what excites us more than anything here. When you have that and customer empathy and deep insights into what the problems are, then you can solve problems using technology.”

Many Silicon Valley startups, by contrast, “have whiz-bang technology and throw it at a problem they often don’t understand,” Lehr said.

IBM’s Watson may end up being the biggest competitor to Merlon. The 600 Promontory consultants it acquired are training the software to handle compliance tasks like AML/KYC. Other competitors include Oracle, SAS and NICE Actimize.

Lehr, however, is anticipating a boost from Watson.

“They’re going to pump tons of marketing dollars into explaining the problem and the need for AI and machine learning in this domain. But how much uptake does Watson have in financial services? How much of what they sell is actually consulting services versus a product?” Lehr said.

Investors like Work-bench and Nyca see dollar signs — all told, banks spend $100 billion on people and technology focused on compliance every year. That doesn’t include all the fines banks sometimes incur related to AML/KYC issues. On Monday, Citi agreed to pay a $97 millionfine to settle allegations that its Banamex subsidiary didn’t do enough to prevent money laundering. Earlier this year, Deutsche Bank paid a $425 million fine for failing to detect $10 billion allegedly being laundered out of Russia.

But Merlon’s backers also see an opportunity for the greater good to deter crime.

As an investor, Morris looks for solutions that will deliver a return on investment through expense cuts and efficiencies, while working within the existing complex environment of legacy systems. At the same time, he is looking for companies that can help establish how the systems could work in the future.

“You could tell a major financial institution, the whole thing could look like this, but we can start now, and you don’t need to make all the decisions today,” Morris said. “It will deliver value today, but three or four years from now, this could become the way the whole analysis/process is done.”

Lehr, whose company focuses on enterprise software, said he listened to banks’ pain points to figure out where money could best be used to solve problems.

“If we look at KYC alone, there’s five or six different systems a compliance analyst will have to use today. None of them are connected,” he said. He added, “We were looking for software that could aggregate these disparate tools and leverage machine learning to reduce false positives.”

Merlon will “turn that compliance analyst into a supercharged person because they’re able to do the job of multiple analysts and spend their time on the areas where there is a need to dive further and pull out that noise, whether it’s a false positive or just something that’s irrelevant,” Lehr said.

Regulators have become more open-minded about the applications of new technology, Morris observed.

“Every agency is aware of it and has coherent groups that talk about it,” Morris said. “They are getting exposed to the trends, engaging at conferences and meeting companies.”